
server = 'mando'
if (server == 'jabba'){
    data_path = '/data3/hratch/norcross_abc/'
    gpu_use = F
}else if (server == 'mando'){
    data_path = '/data/hratch/norcross_abc/'
    gpu_use = T
}

library(reticulate, quietly = T)
if (gpu_use){
    device<-'cuda:0'
    tensorly <- reticulate::import('tensorly')
    tensorly$set_backend('pytorch')
}else{
    device<-NULL
}

n.cores<-30
env_name = 'norcross_abc'


suppressPackageStartupMessages({
    suppressWarnings({
        library(liana, quietly = T)
        library(decoupleR, quietly = T)

        library(Seurat, quietly = T)
        
        library(dplyr, quietly = T)
        library(tidyr, quietly = T)
        library(magrittr, quietly = T)
        library(tibble, quietly = T)
        library(reshape2, quietly = T)
        library(stringr)
        library(plyr, quietly = T)
        library(textshape, quietly = T)
        
        library(ggplot2, quietly = T)
        library(circlize, quietly = T)
        library(RColorBrewer, quietly = T)
        
        # library(textshape, quietly = T)
        c2c <- reticulate::import(module = "cell2cell", as="c2c")
    })
})


params<-list(return_all = FALSE, #whether to return all LRs in liana after filtering for expr_prop
             filter_specificity = FALSE, # whether to filter scores by specificty
             filter_thresh = 0.05, # specificity filter threshold
             filter_specificity_how = 'fill', # or exclude -- fill insignifanct interactions with min or remove alltogether
             lr_fill_by = NaN, # or 0 or NaN -- what to use in the lr_fill tc2c argument
             how = 'outer' # what to use in the how tc2c argument
            )


abc.integrated<-readRDS(paste0(data_path, 'processed/abc_annotated.RDS'))


cell.types<-c('T-cells', 'cDC_1', 'cDC_2', 'Xcr1+ DC_1', 'Xcr1+ DC_2', 'pDC', 'ncMono', 'Mf')
contexts<-c('ABC', 'DT_ABC')
abc.ccc<-subset(abc.integrated, subset = (Cell.Type.Level1 %in% cell.types) & (Cell.Type.Level2 != 'gd-T') &
                                        (orig.ident %in% contexts))
Idents(abc.ccc)<-'Cell.Type.Level2'


# format into sce object
DefaultAssay(abc.ccc)<-'RNA'
abc.ccc@meta.data <-abc.ccc@meta.data %>% droplevels()
abc.ccc.sce<-Seurat::as.SingleCellExperiment(abc.ccc)


# map LIANA consensus resource to mouse
suppressMessages({
    suppressWarnings({
        op_resource <- liana::select_resource("Consensus")[[1]]

        # Generate orthologous resource
        ortholog_resource <- liana::generate_homologs(op_resource = op_resource,
                                               target_organism = 10090) # mouse
    })
})


# suppressWarnings({
#     abc.ccc.sce<-liana_bysample(sce = abc.ccc.sce, 
#                idents_col = 'Cell.Type.Level2', 
#                sample_col = 'orig.ident',
#                assay.type='logcounts', # log-normalized counts
#                verbose = T, 
#               aggregate_how = 'both', # consensus magnitude AND specifity score
#                 resource = 'custom', 
#                 external_resource = ortholog_resource, # consensus resource in mouse
#                min_cells = 5, # default min cells to be considered
#                expr_prop = 0.05, # expressed in atleast 5% of cells to be scored
#                return_all = params$return_all, # assign a low score to those that don't pass expr_prop
#                 parallelize = TRUE, workers = n.cores,
#               )
# })
# saveRDS(abc.ccc.sce@metadata$liana_res, paste0(data_path, 'interim/', 'v2_liana_bysample.rds'))

abc.ccc.sce@metadata$liana_res<-readRDS(paste0(data_path, 'interim/', 'v2_liana_bysample.rds'))


sapply(abc.ccc.sce@metadata$liana_res, function(x) dim(x)[[1]])
       
if (params$filter_specificity_how == 'exclude'){
        sapply(abc.ccc.sce@metadata$liana_res, function(x) dim(x[x$specificity_rank <= params$filter_thresh, ])[[1]])
    }else if (params$filter_specificity_how == 'fill'){
        sapply(abc.ccc.sce@metadata$liana_res, function(x) dim(x[x$specificity_rank > params$filter_thresh, ])[[1]])
    }


if (params$filter_specificity){
    liana_res<-list()
    for (sample.name in names(abc.ccc.sce@metadata$liana_res)){
        x<-abc.ccc.sce@metadata$liana_res[[sample.name]]
        if (params$filter_specificity_how == 'exclude'){
            x<-x[x$specificity_rank <= params$filter_thresh, ] # eliminate those under threshold
        }else if (params$filter_specificity_how == 'fill'){
            x[x$specificity_rank > params$filter_thresh, 'magnitude_rank']<-max(x$magnitude_rank) # replace with "lowest" value (max because magnitude rank orders lower values as higher importance)
        }
        abc.ccc.sce@metadata$liana_res[[sample.name]]<-x
    }
}


abc.ccc.sce@metadata$liana_res<-liana:::preprocess_scores(context_df_dict = abc.ccc.sce@metadata$liana_res, 
                                 score_col = 'magnitude_rank', # score to preprocess
                                  outer_fraction = 1/3, # only keep cells/LRs present in atleast in 1/3 of contexts
                                                        # outer_fract shouldn't change things since there are only 3 samples
                                  invert = TRUE, # since magnitude has lower scores being more valuable
                                  invert_fun = function(x) 1-x, 
                                  non_negative = TRUE, # shouldn't effect things since all magnitude ranks are positive
                                  non_negative_fill = 0, 
                                  verbose = T
                                 )

Inverting `magnitude_rank`!

Inverting `magnitude_rank`!


score_col = 'magnitude_rank'

if (params$lr_fill_by == 'min'){
    lr.fill.value<-min(sapply(abc.ccc.sce@metadata$liana_res, function(x) min(x[[score_col]])))
}else if (is.numeric(params$lr_fill_by)){
    lr.fill.value<-params$lr_fill_by
}
lr.fill.value


# # build tensor
# tensor <- liana::liana_tensor_c2c(sce = abc.ccc.sce,
#                                   context_df_dict = NULL, 
#                                   score_col = 'magnitude_rank', 
#                                   how = params$how, 
#                                   lr_fill = lr.fill.value,
#                                   cell_fill = 0, 
#                                   context_order = levels(abc.ccc.sce@colData$orig.ident), 
#                                   sort_elements = TRUE, 
#                                   conda_env = env_name, 
#                                   build_only = TRUE, 
#                                   device = device)

# # decompose tensor
# tensor<-liana::decompose_tensor(tensor = tensor, 
#                          rank = NULL, 
#                          tf_optimization = 'regular', # CHANGE TO ROBUST
#                          elbow_metric = 'error', # Metric to use in the elbow analysis.
#                          smooth_elbow = FALSE, # Whether smoothing the metric of the elbow analysis.
#                          upper_rank=25, # Max number of factors to try in the elbow analysis
#                          init = 'svd', # Initialization method of the tensor factorization
#                          factors_only = FALSE, 
#                         )
# reticulate::py_save_object(object = tensor, 
#                            filename = paste0(paste0(data_path, 'processed/', 'v2_ccc_tensor.pkl')))

tensor<-reticulate::py_load_object(paste0(paste0(data_path, 'processed/', 'v2_ccc_tensor.pkl')))


write.csv(tensor$factors$interactions, 
          paste0(data_path, 'interim/', 'v2_lr_loadings.csv'))


# basic formatting
factors<-liana:::format_c2c_factors(tensor$factors)

# ordering categories
f.c<-factors$contexts
f.c[['context']]<-factor(f.c$context, levels(abc.ccc.sce@colData$orig.ident))
factors[['contexts']]<-f.c
for (ct in c('senders', 'receivers')){
    f.ct<-factors[[ct]]
    f.ct[['celltype']]<-factor(f.ct$celltype, levels(abc.ccc.sce@colData$Cell.Type.Level2))
    factors[[ct]]<-f.ct
}
abc.ccc.sce@metadata$tensor_res<-factors

abc.ccc.sce@colData[['group']] = abc.ccc.sce@colData[['orig.ident']] # must have a "group" for plotting


print(paste0('The identified tensor rank is ', tensor$rank))

# Estimate standard error
error_average <- tensor$elbow_metric_raw %>%
    t() %>%
    as.data.frame() %>%
    mutate(rank=row_number()) %>% 
    pivot_longer(-rank, names_to = "run_no", values_to = "error") %>%
    group_by(rank) %>%
    summarize(average = mean(error),
              N = n(),
              SE.low = average - (sd(error)/sqrt(N)),
              SE.high = average + (sd(error)/sqrt(N))
           )

# plot
error_average %>%
    ggplot(aes(x=rank, y=average), group=1) +
    geom_line(col='red') + 
    geom_ribbon(aes(ymin = SE.low, ymax = SE.high), alpha = 0.1) +
    geom_vline(xintercept = tensor$rank, colour='darkblue') + # rank of interest
    theme_bw() +
    labs(y="Error", x="Rank")

[1] "The identified tensor rank is 9"


# addapt from liana
plot_c2c_overview <- function(sce, group_col, sample_col, factors.of.interest=NULL){

    factors <- get_c2c_factors(sce, group_col, sample_col)
    if (is.null(factors.of.interest)){
        factors.of.interest<-names(which(sapply(names(factors$contexts), function(x) startsWith(x, 'Factor.'))))
    }

    # Contexts
    contexts <- factors$contexts %>%
        pivot_longer(cols = -c("context", group_col),
                     names_to = "factor", values_to = "loadings"
        ) %>%
        filter(factor %in% factors.of.interest) %>%
        ggplot(aes(x=context, y=loadings, fill=.data[[group_col]])) +
        geom_bar(stat="identity") +
        facet_grid(factor ~ .) +
        theme_bw(base_size = 14) +
        theme(axis.title.x=element_blank(),
              axis.text.x=element_blank(),
              axis.ticks.x=element_blank(),
              strip.text.y = element_blank(),
              plot.title = element_text(hjust = 0.5)) +
        ggtitle('Contexts') +
        ylab(NULL)

    # lr
    lr <- factors$interactions %>%
        pivot_longer(-lr, names_to = "factor", values_to = "loadings") %>%
        filter(factor %in% factors.of.interest) %>%
        ggplot(aes(x=lr, y=loadings)) +
        geom_bar(stat="identity") +
        facet_grid(factor ~ ., scales = "free_y") +
        theme_bw(base_size = 14) +
        theme(axis.title.x=element_blank(),
              axis.text.x=element_blank(),
              axis.ticks.x=element_blank(),
              strip.background = element_blank(),
              strip.text.y = element_blank(),
              plot.title = element_text(hjust = 0.5)) +
        ggtitle('Interactions') +
        ylab(NULL)


    # Sender cells
    senders <- factors$senders %>%
        pivot_longer(cols = -celltype,
                     names_to = "factor", values_to = "loadings"
        ) %>%
        filter(factor %in% factors.of.interest) %>%
        ggplot(aes(x=celltype, y=loadings,
                   fill=celltype)) +
        geom_bar(stat="identity") +
        facet_grid(factor ~ .) +
        theme_bw(base_size = 14) +
        theme(axis.title.x=element_blank(),
              axis.text.x=element_blank(),
              axis.ticks.x=element_blank(),
              strip.background = element_blank(),
              strip.text.y = element_blank(),
              plot.title = element_text(hjust = 0.5)) +
        ylab(NULL) +
        ggtitle('Senders')

    # Receiver cells
    receivers <- factors$receivers %>%
        pivot_longer(cols = -celltype,
                     names_to = "factor", values_to = "loadings"
        ) %>%
        filter(factor %in% factors.of.interest) %>%
        ggplot(aes(x=celltype, y=loadings,
                   fill=celltype)) +
        geom_bar(stat="identity") +
        facet_grid(factor ~ .) +
        theme_bw(base_size = 14) +
        theme(axis.title.x=element_blank(),
              axis.text.x=element_blank(),
              strip.background = element_blank(),
              axis.ticks.x=element_blank(),
              strip.text.y = element_text(size=15, face = "bold"),
              plot.title = element_text(hjust = 0.5)) +
        ylab(NULL) +
        ggtitle('Receivers')

    # Assemble overview plot
    overview <- patchwork::wrap_plots(list(contexts,
                                           lr,
                                           senders,
                                           receivers
    ),
    ncol=4,
    nrow(1)) +
        patchwork::plot_layout(guides = "collect")

    grid::grid.draw(overview)
}


h_ = 16
w_ = 15
options(repr.plot.height=h_, repr.plot.width=w_)

plot_c2c_overview(sce = abc.ccc.sce, group_col = 'group', sample_col = 'orig.ident')

Warning message:
“Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
  # Was:
  data %>% select(group_col)

  # Now:
  data %>% select(all_of(group_col))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.”


h_ = 10
w_ = 15
options(repr.plot.height=h_, repr.plot.width=w_)
fos<-c('3', '8', '9')
plot_c2c_overview(sce = abc.ccc.sce, group_col = 'group', sample_col = 'orig.ident', 
                 factors.of.interest = paste0('Factor.', fos))


calculate_gini(factors$senders) %>% arrange(gini)


calculate_gini(factors$receivers) %>% arrange(gini)


h_ = 25
w_ = 10
n_lrs<-15#5
options(repr.plot.height=h_, repr.plot.width=w_)
liana::plot_lr_heatmap(sce = abc.ccc.sce,  n = n_lrs)


top_lrs = list()
for (factor.name in paste0('Factor ', fos)){
    top_lrs[[factor.name]] = tensor$get_top_factor_elements(order_name='interactions', 
                                factor_name=factor.name, 
                                top_number=as.integer(n_lrs))
}
top_lrs


cc_plots<-list()
for (selected_factor in paste0('Factor.', fos)){
    cc_plots[[selected_factor]]<-liana::plot_c2c_cells(sce = abc.ccc.sce,
                                   factor_of_int = selected_factor,
                                    name = paste0(selected_factor, '\n', 
                                                 "Loadings Product"))    
}


h_ = 6
w_ = 8
options(repr.plot.height=h_, repr.plot.width=w_)
i<-1
cc_plots[[i]]
i<-i+1


cc_plots[[i]]
i<-i+1


cc_plots[[i]]
i<-i+1


get_joint_loadings<-function(tensor, selected_factor, dim1 = 'senders', dim2 = 'receivers'){
    
    v1<-tensor$factors[[dim1]][[selected_factor]]
    names(v1)<-rownames(tensor$factors[[dim1]])

    v2<-tensor$factors[[dim2]][[selected_factor]]
    names(v2)<-rownames(tensor$factors[[dim2]])
    
    return(outer(v1, v2, "*"))
}

get_lr_by_cell_pairs<-function(tensor, selected_factor, n_cci = NULL, n_lrs = NULL){
    sr<-get_joint_loadings(tensor, selected_factor, dim1 = 'senders', dim2 = 'receivers')
    
    # flatten
    sr<-as.vector(sr)
    element.names<-expand.grid(rownames(tensor$factors$senders), rownames(tensor$factors$receivers))
    element.names<-paste0(element.names[[1]], '->', element.names[[2]])
    names(sr)<-element.names
    
    if (!is.null(n_cci)){
#         sr<-sr[sr > cci_threshold]
        sr<-sort(sr, decreasing = T)[1:n_cci]
    }
    
    # get outer with lr
    v2<-tensor$factors$interactions[[selected_factor]]
    names(v2)<-rownames(tensor$factors$interactions) 
    if (!is.null(n_lrs)){
        v2<-sort(v2, decreasing = T)[1:n_lrs]
    }

    mat<-outer(sr, v2, "*")
    
    return(t(mat))
}


lr_cc_list<-list()
for (selected_factor in paste0('Factor ', fos)){
    mat<-get_lr_by_cell_pairs(tensor, selected_factor, n_cci = 30, n_lrs = 30)
    write.csv(mat, paste0(data_path, 'interim/', 'Factor', selected_factor, '_lr_by_cell.csv'))
    lr_cc_list[[selected_factor]]<-ComplexHeatmap::Heatmap(mat, 
                                                   name = paste0(selected_factor), 
                                                   column_title = "lr", row_title = 'cci')
}


h_ = 10
w_ = 8
options(repr.plot.height=h_, repr.plot.width=w_)

i<-1
lr_cc_list[[i]]


i<-i+1
lr_cc_list[[i]]


i<-i+1
lr_cc_list[[i]]


organism = 'mouse' 
pathwaydb = 'GOBP' # KEGG# can't use KEGG with mouse, may want to map ortholog

# lr_pairs <- liana::select_resource('Consensus')
# lr_pairs <- lr_pairs$Consensus[c('source_genesymbol', 'target_genesymbol')]
lr_pairs <- ortholog_resource[c('source_genesymbol', 'target_genesymbol')]
names(lr_pairs) <- c('ligand', 'receptor')
lr_list <- apply(lr_pairs, 1, function(x) paste0(x[[1]], '^', x[[2]]))
                 
lr_set <- c2c$external$generate_lr_geneset(lr_list = lr_list, 
                        complex_sep='_', # Separation symbol of the genes in the protein complex
                          lr_sep='^', # Separation symbol between a ligand and a receptor complex
                          organism=organism,
                          pathwaydb=pathwaydb,
                          readable_name=TRUE)    
lr_loadings = factors[['interactions']] %>% 
                column_to_rownames("lr")


gsea_res <- c2c$external$run_gsea(loadings=lr_loadings, 
                               lr_set=lr_set,
                               output_folder=paste0(data_path, 'interim/')
                              )
names(gsea_res)<-c('pvals', 'scores', 'gsea_df')


gsea.dotplot<-function(pval_df, score_df, significance = 0.05, font_size = 15){
    pval_df <- pval_df[apply(pval_df<significance,1,any), apply(pval_df<significance, 2, any)] %>% 
            mutate_all(function(x) -1*log10(x + 1e-9))
    score_df <- score_df[rownames(pval_df), colnames(pval_df)]
    pval_df <- pval_df %>%
                    tibble::rownames_to_column('Annotation')
    score_df <- score_df %>%
                    tibble::rownames_to_column('Annotation')

    viz_df <- cbind(melt(pval_df), melt(score_df)[[3]])
    names(viz_df) <- c('Annotation', 'Factor', 'Significance', 'NES')
    
                       
    dotplot <- ggplot(viz_df, aes(x = Factor, y = Annotation)) + geom_point(aes(size = Significance, color = NES)) + 
                scale_color_gradient2() + theme_bw(base_size = font_size) + 
                theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=0.5))+
                       scale_size(range = c(0, 8)) + 
                       guides(size=guide_legend(title="-log10(p-value)"))
    return(dotplot)
}


min(min(gsea_res$pvals))


h_ = 8
w_ = 10
options(repr.plot.height=h_, repr.plot.width=w_)

dotplot <- gsea.dotplot(pval_df = gsea_res$pvals, 
                       score_df = gsea_res$scores, 
                       significance = 0.05)
dotplot


# p.adjust = BH corrected -- # https://github.com/YuLab-SMU/clusterProfiler/issues/124
# GeneRatio -- ratio of input genes in specific pathway vs input genes in all pathways https://www.biostars.org/p/220465/

# can improve visualization by directly manipulating the @result of each output
# if doing this, change qvalueCutoff to 1 and directly filter

visualize_ora<-function(cp.out, factor.name, sig.thresh = 0.1, top_n_terms = 10){
    viz.df<-cp.out@result
    viz.df[['significant']]<-paste0('fdr > ', sig.thresh)
    viz.df[viz.df$p.adjust <= sig.thresh, 'significant']<-paste0('fdr', ' \u2264 ',  sig.thresh)

    viz.df[['significant']]<-factor(viz.df[['significant']], 
                                    levels = c(paste0('fdr', ' \u2264 ',  sig.thresh), paste0('fdr > ', sig.thresh)))
    viz.df[['log.fdr']]<- -log10(viz.df$p.adjust)
    viz.df[['GeneRatio2']]<-unlist(unname(sapply(viz.df$GeneRatio, function(x) eval(parse(text=x)))))

    viz.df<-viz.df[with(viz.df, order(significant, -GeneRatio2, -log.fdr, -Count)), ]

    viz.df<-viz.df[1:top_n_terms, ]
    viz.df[['ID']]<-factor(viz.df[['ID']], 
                                    levels = rev(viz.df[['ID']]))

    green_hex = brewer.pal(n = 11, name ='RdYlGn')[[11]]
    yellow_hex = brewer.pal(n = 11, name = 'RdYlGn')[[6]]
    red_hex = brewer.pal(n = 11, name = 'RdYlGn')[[1]]

    lower_q = min(viz.df$log.fdr[!(is.na(viz.df$log.fdr))])
    upper_q = max(viz.df$log.fdr[!(is.na(viz.df$log.fdr))])
    middle_q = mean(c(lower_q, upper_q))#median(viz.df$log.fdr[!(is.na(viz.df$log.fdr))]) 
    # middle_q = 7.5

    g<-ggplot(data = viz.df, aes(x = GeneRatio2, y = ID, color = log.fdr, size = Count, shape = significant)) + 
    geom_point() + 
    ylab('') + xlab('GeneRatio') + ggtitle(factor.name) +
#     scale_color_gradient2(low = green_hex, mid = yellow_hex, high = red_hex, name = '-log10(fdr)', 
#                          limits = c(lower_q, upper_q), midpoint = middle_q) + 
    scale_colour_gradientn(colours = rev(c("darkred", "orange", "yellow")), name = '-log10(fdr)')+
    scale_shape_manual(values = c(16,4), drop = FALSE)+
    scale_size_continuous(range = c(4,12))+
    theme_bw() + theme(#axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), 
                      text = element_text(size = 15), plot.title = element_text(hjust = 0.5))


    return(g)
}
                                                 
visualize_ora_all<-function(ora_res, sig.thresh = 0.1, top_n_terms = 15)     {
                             
    viz.df <- data.frame(matrix(ncol = 10))
    colnames(viz.df)<-c('ID','Description','GeneRatio','BgRatio','pvalue','p.adjust','qvalue','geneID','Count', 'Comparison')

    for (comp.name in names(ora_res))({
        viz.df.sub<-ora_res[[comp.name]]@result
        viz.df.sub[['Comparison']]<-comp.name
        viz.df<-rbind(viz.df, viz.df.sub)
    })

    viz.df<-viz.df[-1,]
    rownames(viz.df)<-1:dim(viz.df)[[1]]

    viz.df[['significant']]<-paste0('fdr > ', sig.thresh)
    viz.df[viz.df$p.adjust <= sig.thresh, 'significant']<-paste0('fdr', ' \u2264 ',  sig.thresh)

    viz.df[['significant']]<-factor(viz.df[['significant']], 
                                    levels = c(paste0('fdr', ' \u2264 ',  sig.thresh), paste0('fdr > ', sig.thresh)))
    viz.df[['log.fdr']]<- -log10(viz.df$p.adjust)
    viz.df[['GeneRatio2']]<-unlist(unname(sapply(viz.df$GeneRatio, function(x) eval(parse(text=x)))))

    viz.df<-viz.df[with(viz.df, order(significant, -GeneRatio2, -log.fdr, -Count)), ]

    n_terms<-top_n_terms
    unique_n_terms<-length(unique(viz.df[1:n_terms, 'ID']))
    while ((unique_n_terms < top_n_terms) & (n_terms < dim(viz.df)[[1]])){
        n_terms <- n_terms + 1
        unique_n_terms<-length(unique(viz.df[1:n_terms, 'ID']))
    }
    viz.df<-viz.df[1:n_terms, ]

    green_hex = brewer.pal(n = 11, name ='RdYlGn')[[11]]
    yellow_hex = brewer.pal(n = 11, name = 'RdYlGn')[[6]]
    red_hex = brewer.pal(n = 11, name = 'RdYlGn')[[1]]

    lower_q = min(viz.df$log.fdr[!(is.na(viz.df$log.fdr))])
    upper_q = max(viz.df$log.fdr[!(is.na(viz.df$log.fdr))])
    middle_q = mean(c(lower_q, upper_q))#median(viz.df$log.fdr[!(is.na(viz.df$log.fdr))]) 
    # middle_q = 7.5

    g<-ggplot(data = viz.df, aes(x = Comparison, y = ID, color = log.fdr, size = GeneRatio2, shape = significant)) + 
    geom_point() + 
    ylab('') + xlab('') + ggtitle('') +
    #     scale_color_gradient2(low = green_hex, mid = yellow_hex, high = red_hex, name = '-log10(fdr)', 
    #                          limits = c(lower_q, upper_q), midpoint = middle_q) + 
    scale_colour_gradientn(colours = rev(c("darkred", "orange", "yellow")), name = '-log10(fdr)')+
    scale_shape_manual(values = c(16,4), drop = FALSE)+
    scale_size_continuous(range = c(4,12), name = 'Gene Ratio')+
    theme_bw() + theme(#axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), 
                      text = element_text(size = 20), plot.title = element_text(hjust = 0.5))
    return(g)
}


# prep input
keys<-names(lr_set)

lr_set_R<-list()
for (key in keys){
    lr_set_R[[key]]<-reticulate::iterate(reticulate::as_iterator(lr_set[key]))
} 

ora_db<-stack(lr_set_R)
ora_db<-ora_db[c(2,1)]
names(ora_db)<-c('Pathway', 'LR_Pair')


# run ORA on top LRs per factor
frac_top = 0.1 # get the top n% of LRs per factor
n_lrs_top<-round(frac_top*dim(factors$interactions)[[1]])

ora_res<-list()
for (factor.name in paste0('Factor ', 1:tensor$rank)){
    factor_lrs<-names(tensor$get_top_factor_elements(order_name='interactions', 
                                factor_name=factor.name, 
                                top_number=as.integer(n_lrs_top)))
    
    if (length(setdiff(factor_lrs, lr_list)) > 0){
        stop('Unexpected LRs in loadings')
    }
    
    # if universe argument is not set, all the LRs in ora_db (which is all the LRs from the DB that mapped
    # to a pathway) will be used
    ora_res[[factor.name]]<-clusterProfiler::enricher(factor_lrs, TERM2GENE = ora_db, 
                                                     pAdjustMethod = 'BH', 
                                                      pvalueCutoff = 1, # will filter later
                                                      qvalueCutoff = 1 # will filter later
                                                     )
}


# visualize
dotplot_list<-c()
for (fn in names(ora_res)){
    cp.out<-ora_res[[fn]]
    g<-visualize_ora(cp.out, fn)
    
    dotplot_list[[fn]]<-g
    

}

h_ = 22
w_ = 35
options(repr.plot.height=h_, repr.plot.width=w_)

suppressWarnings({
    g<-cowplot::plot_grid(dotplot_list[[1]], dotplot_list[[2]], dotplot_list[[3]], dotplot_list[[4]], dotplot_list[[5]], 
                   dotplot_list[[6]], dotplot_list[[7]], dotplot_list[[8]], dotplot_list[[9]],
                   ncol = 3)
})

g


h_ = 13
w_ = 23
options(repr.plot.height=h_, repr.plot.width=w_)

g<-visualize_ora_all(ora_res, sig.thres = 0.1, top_n_terms = 20)
g


# run ORA on top LRs per factor
frac_top = 0.1 # get the top n% of LRs per factor
n_lrs_top<-round(frac_top*dim(factors$interactions)[[1]])

ora_res_strict<-list()
for (factor.name in paste0('Factor ', 1:tensor$rank)){
    factor_lrs<-names(tensor$get_top_factor_elements(order_name='interactions', 
                                factor_name=factor.name, 
                                top_number=as.integer(n_lrs_top)))
    
    if (length(setdiff(factor_lrs, lr_list)) > 0){
        stop('Unexpected LRs in loadings')
    }
    
    # if universe argument is not set, all the LRs in ora_db (which is all the LRs from the DB that mapped
    # to a pathway) will be used
    
    # for now, set background to all SCORED lrs, which is a subset of ora_db
    ora_res_strict[[factor.name]]<-clusterProfiler::enricher(factor_lrs, TERM2GENE = ora_db, 
                                                      universe = rownames(tensor$factors$interactions), 
                                                     pAdjustMethod = 'BH', 
                                                      pvalueCutoff = 1, # will filter later
                                                      qvalueCutoff = 1 # will filter later
                                                     )
}


# visualize
dotplot_list_strict<-c()
for (fn in names(ora_res_strict)){
    cp.out<-ora_res_strict[[fn]]
    g<-visualize_ora(cp.out, fn)
    
    dotplot_list_strict[[fn]]<-g
    

}


h_ = 22
w_ = 35
options(repr.plot.height=h_, repr.plot.width=w_)

suppressWarnings({
    g<-cowplot::plot_grid(dotplot_list_strict[[1]], dotplot_list_strict[[2]], dotplot_list_strict[[3]], dotplot_list_strict[[4]], dotplot_list_strict[[5]], 
                   dotplot_list_strict[[6]], dotplot_list_strict[[7]], dotplot_list_strict[[8]], dotplot_list_strict[[9]],
                   ncol = 3)
})

g


h_ = 13
w_ = 23
options(repr.plot.height=h_, repr.plot.width=w_)

g<-visualize_ora_all(ora_res, sig.thres = 0.1, top_n_terms = 20)
g


lr_pair_genes<-unique(c(lr_pairs$ligand, lr_pairs$receptor)) # lr_pairs is the full LR DB that liana was run on
lr_pair_genes<-unique(unlist(str_split(as.character(lr_pair_genes), '_'))) # complexes joined by _
dataset.genes<-rownames(abc.ccc@assays$RNA@data)

universe.background<-intersect(lr_pair_genes, dataset.genes) 

# get all teh scored lrs and convert LRs --> genes
scored_lrs<-unique(unlist(str_split(as.character(factors$interactions$lr), '\\^'))) # interactions joined by ^
scored_lrs<-unique(unlist(str_split(as.character(scored_lrs), '_'))) # complexes joined by _


cond1<-(length(scored_lrs) > length(lr_pair_genes))
cond2<-(length(setdiff(scored_lrs, lr_pair_genes)) > 0)
cond3<-(length(setdiff(scored_lrs, dataset.genes)) > 0)
if ((cond1) | (cond2) | (cond3)){
    stop('Unexpected difference in identified loadings and LR DB')
}


frac_top = 0.1 # get the top n% of LRs per factor
n_lrs_top<-round(frac_top*dim(factors$interactions)[[1]])

metascape_input = list()
# set background as all lr pairs from database that are present in sc dataset
metascape_input[['_BACKGROUND']]<-universe.background

for (factor.name in paste0('Factor ', 1:tensor$rank)){
    factor_lrs<-names(tensor$get_top_factor_elements(order_name='interactions', 
                                factor_name=factor.name, 
                                top_number=as.integer(n_lrs_top)))
    # LRs --> genes
    factor_genes<-unique(unlist(str_split(as.character(factor_lrs), '\\^'))) # LR interactions joined by ^
    factor_genes<-unique(unlist(str_split(as.character(factor_genes), '_'))) # complexes joined by _
    metascape_input[[factor.name]]<-factor_genes
}

# format input to metascape
max.length <- max(sapply(metascape_input, length))
metascape_input <- lapply(metascape_input, function(v) { c(v, rep(NA, max.length-length(v)))})
metascape_input<-do.call(cbind, metascape_input)
write.csv(metascape_input, paste0(data_path, 'interim/', 'lr_metascape_input_v2.csv'), row.names=FALSE)


format.ms<-function(ms, val_col){
    val_df<-as.data.frame(pivot_wider(data = ms, id_cols = 'GeneList', names_from = 'Description', 
                                      values_from = val_col, 
                                    values_fn = mean))
    val_df<-t(column_to_rownames(x = val_df, loc = 1))

    return(val_df)
}


ms.top20<-read.csv(paste0(data_path, 'interim/', 'lr_metascape_out_v2/Enrichment_heatmap/HeatmapSelectedGO.csv'))
ms.top100<-read.csv(paste0(data_path, 'interim/', 'lr_metascape_out_v2/Enrichment_heatmap/HeatmapSelectedGOTop100.csv'))
ms<-read.csv(paste0(data_path, 'interim/', 'lr_metascape_out_v2/Enrichment_GO/GO_AllLists.csv'))


ms %>%
  dplyr::group_by(GeneList, Description) %>%
  dplyr::summarise(n = dplyr::n(), .groups = "drop") %>%
  dplyr::filter(n > 1L)


ms[(ms$Description == 'Notch signaling pathway') & (ms$GeneList %in% c('Factor 6', 'Factor 8', 'Factor 9')), ]


'Notch signaling pathway' %in% ms.top100$Description


freq<-format.ms(ms = ms, val_col = 'X.InGO')[ms.top20$Description, paste0('Factor ', 1:tensor$rank)]
pvals<-format.ms(ms = ms, val_col = 'Log.q.value.')[ms.top20$Description, paste0('Factor ', 1:tensor$rank)]

pvals<-as.data.frame(pvals)
pvals[['Enrichment.Term']]<-rownames(pvals)
freq<-as.data.frame(freq)
freq[['Enrichment.Term']]<-rownames(freq)

pvals<-melt(pvals, id.vars = 'Enrichment.Term', value.name = 'log10p', variable.name = 'Condition')
freq<-melt(freq, id.vars = 'Enrichment.Term', value.name = 'Frequency', variable.name = 'Condition')
pvals[['log10p']] <- -as.numeric(pvals[['log10p']]) 

viz.df <- cbind(pvals, Frequency = as.numeric(freq$Frequency))


h_ = 10
w_ = 25
options(repr.plot.height=h_, repr.plot.width=w_)

green_hex = brewer.pal(n = 11, name ='RdYlGn')[[11]]
yellow_hex = brewer.pal(n = 11, name = 'RdYlGn')[[6]]
red_hex = brewer.pal(n = 11, name = 'RdYlGn')[[1]]

lower_q = min(viz.df$log10p[!(is.na(viz.df$log10p))])
upper_q = max(viz.df$log10p[!(is.na(viz.df$log10p))])
middle_q = mean(c(lower_q, upper_q)) # median(viz.df$log10p[!(is.na(viz.df$log10p))]) # 
# middle_q = 7.5

g<-ggplot(data = viz.df, aes(x = Condition, y = Enrichment.Term, color = log10p, size = Frequency)) + 
geom_point() + 
# scale_color_gradient2(low = green_hex, mid = yellow_hex, high = red_hex, name = '-log10(q-val)', 
#                      limits = c(lower_q, upper_q), midpoint = middle_q) + 
scale_colour_gradientn(colours = rev(c("darkred", "orange", "yellow")), name = '-log10(q-val)')+
scale_size_continuous(range = c(1,15)) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), 
                  text = element_text(size = 20))


for (ext in c('.svg', '.png', '.pdf')){
    fn<-paste0(data_path, 'figures/', 'lr_metascape_enrichment_dotplot', ext)
    ggsave(fn, g, height = h_, width = w_)}

g

Warning message:
“Removed 25 rows containing missing values (`geom_point()`).”
Warning message:
“Removed 25 rows containing missing values (`geom_point()`).”
Warning message:
“Removed 25 rows containing missing values (`geom_point()`).”
Warning message:
“Removed 25 rows containing missing values (`geom_point()`).”


# obtain progeny gene sets
progeny <- decoupleR::get_progeny(organism = 'mouse', top=5000) %>%
    select(-p_value)

# convert to LR sets
progeny_lr <- generate_lr_geneset(sce = abc.ccc.sce,
                                  resource = progeny)


h_ = 5
w_ = 12
options(repr.plot.height=h_, repr.plot.width=w_)

# interaction loadings to matrix
mat <- factors$interactions[, c('lr', paste0('Factor.', fos))] %>%
    column_to_rownames("lr") %>%
  as.matrix()

# run enrichment analysis with decoupler
# (we fit a univariate linear model for each gene set)
# We don't consider genesets with minsize < 10
res <- decoupleR::run_ulm(mat = mat,
                          network = progeny_lr,
                          .source = "set",
                          .target = "lr",
                          minsize=10) %>%
  mutate(p_adj = p.adjust(p_value, method = "fdr"))

res %>% # sig/isnig flag
  mutate(significant = if_else(p_adj <= 0.1, "signif.", "not")) %>%
  ggplot(aes(x=source, y=condition, shape=significant,
             colour=score, size=-log10(p_value+1e-36))) +
  geom_point() +
  scale_colour_gradient2(high = "red", low="blue") +
  scale_size_continuous(range = c(3, 12)) +
  scale_shape_manual(values=c(21, 16)) +
  theme_bw(base_size = 15) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  labs(x="Pathway",
       y="Factor",
       colour="Activity"
       )


# progeny_lr[(progeny_lr$set == 'Estrogen') & (progeny_lr$mor > 0), ]


pathway = 'JAK-STAT'
factor = 'Factor.8'
# Plot LRs associated with Estrogen
lrs <-  factors$interactions %>%
  left_join(progeny_lr, by="lr") %>%
  filter(set==pathway) %>%
  select(lr, set, mor, loading = factor) %>%
  mutate(lr = gsub(as.character(str_glue("\\^")), " -> ", lr)) %>%
  mutate(weight = if_else(mor >= 0, "positive", "negative"))
lrs %>%
  # only label those that are > x
  mutate(lr = if_else(loading>=0.001 & abs(mor) > 2, lr, "")) %>%
  ggplot(aes(x=mor, y=loading, colour=weight)) +
  # label only top 20
  stat_smooth(method = "lm", col = "red") +
  geom_point(alpha = 0.5) + 
  ggrepel::geom_label_repel(aes(label = lr)) +
  theme_bw(base_size = 15) +
  scale_colour_manual(values = c("royalblue3", "red")) +
  labs(x="Pathway Weight", y="LR Loading")

factor	gini
<chr>	<dbl>
Factor.4	0.04662789
Factor.2	0.05245001
Factor.1	0.07457592
Factor.3	0.08376764
Factor.7	0.10152487
Factor.8	0.12061640
Factor.9	0.14044531
Factor.6	0.34656555
Factor.5	0.68959615

factor	gini
<chr>	<dbl>
Factor.5	0.1723785
Factor.6	0.4520889
Factor.2	0.5527723
Factor.4	0.6069823
Factor.3	0.6185852
Factor.9	0.6444110
Factor.1	0.6505925
Factor.8	0.8695110
Factor.7	0.9298938

	Category	CategoryID	GO	Description	PARENT_GO	LogP	Enrichment	Z.score	X.TotalGeneInLibrary	X.GeneInGO	X.GeneInHitList	X.GeneInGOAndHitList	X.InGO	STDV..InGO	GeneID	Hits	Log.q.value.	GeneList
	<chr>	<int>	<chr>	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<chr>	<chr>	<dbl>	<chr>
1665	WikiPathways	27	WP29	Notch signaling pathway		-2.9	4.4	4.2	1200	14	118	6	5.1	2.0	11491\|17305\|18128\|19164\|21926\|59287	Adam17\|Mfng\|Notch1\|Psen1\|Tnf\|Ncstn	-1.8	Factor 6
1808	GO Biological Processes	19	GO:0007219	Notch signaling pathway	19_GO:0050896 response to stimulus	-2.1	2.8	3.1	1200	25	118	7	5.9	2.2	11487\|11491\|18128\|19164\|21803\|21813\|59287	Adam10\|Adam17\|Notch1\|Psen1\|Tgfb1\|Tgfbr2\|Ncstn	-1.3	Factor 6
2306	GO Biological Processes	19	GO:0007219	Notch signaling pathway	19_GO:0050896 response to stimulus	-2.7	3.1	3.6	1200	25	122	8	6.6	2.2	11487\|11491\|18128\|18129\|19164\|20085\|21803\|21813	Adam10\|Adam17\|Notch1\|Notch2\|Psen1\|Rps19\|Tgfb1\|Tgfbr2	-1.6	Factor 8
2482	WikiPathways	27	WP29	Notch signaling pathway		-2.0	3.5	3.2	1200	14	122	5	4.1	1.8	11491\|17305\|18128\|18129\|19164	Adam17\|Mfng\|Notch1\|Notch2\|Psen1	-1.2	Factor 8
2530	GO Biological Processes	19	GO:0007219	Notch signaling pathway	19_GO:0050896 response to stimulus	-4.1	3.9	5.0	1200	25	123	10	8.1	2.5	11487\|11491\|11820\|13610\|18128\|18129\|19164\|21803\|21813\|59287	Adam10\|Adam17\|App\|S1pr3\|Notch1\|Notch2\|Psen1\|Tgfb1\|Tgfbr2\|Ncstn	-2.6	Factor 9
2550	WikiPathways	27	WP29	Notch signaling pathway		-3.7	4.9	4.9	1200	14	123	7	5.7	2.1	11491\|17305\|18128\|18129\|19164\|21926\|59287	Adam17\|Mfng\|Notch1\|Notch2\|Psen1\|Tnf\|Ncstn	-2.3	Factor 9

Downstream Analysis¶

ORA¶

ORA Clusterprofiler¶

ORA Metascape¶